1. Field of the Invention
The present invention relates to an image processing apparatus, an image processing method, and a storage medium, and particularly, to an image processing apparatus, an image processing method, and a storage medium configured to cluster face images.
2. Description of the Related Art
Conventionally, various techniques for recognizing human faces by image processing are proposed, and in recent years, the techniques are widely used in products of digital cameras, etc.
For example, Japanese Patent Application Laid-Open Publication No. 2007-140823 proposes a technique for highly accurate face verification to verify human faces from still image data, even if image photographing conditions change. In the proposed technique, data of similarity and threshold are corrected in accordance with the photographing conditions to allow highly accurate face verification, even if the photographing conditions are poor.
Furthermore, for example, Japanese Patent Application Laid-Open Publication No. 2009-42876 proposes applications of a recent face detection technique for video images. One of the applications includes a video indexing technique, and a technique is proposed to improve the identification performance of faces, in which face orientations of performers are taken into consideration to classify performing scenes of each performer in video image data.
The proposed technique is a method in which the photographing conditions, etc., do not have to be analyzed. In the technique, face states that change depending on face orientations, etc., are identified, and image patterns classified for each face state are used to improve the identification performance and prevent an oversight of a performing scene.
In the latter technique, when a face is detected, a face image clustering process for clustering the detected face image data is executed. In the face image clustering, a similarity between two face images A and B is calculated, and if the similarity is over a predetermined threshold, a process of determining the face images A and B as face images of a same person is executed.
However, if the predetermined threshold is set low, different persons are classified into same groups, and if the threshold is set high, same persons are unlikely to be classified into same groups. Therefore, there is a problem that optimal setting is not easy. This is because, in face image patterns of various persons, there are faces in which the similarities with other persons tend to be high (in other words, faces that tend to look like other persons), and there are faces in which the similarities with other persons tend to be low (in other words, faces that are unlikely to look like other persons).